Cpu Profile

If the user wants to see recent slow queries:

SELECT
    query_id,
    query_duration_ms,
    formatReadableSize(memory_usage) AS peak_memory,
    left(query, 120) AS query_preview
FROM system.query_log
WHERE type = 'QueryFinish'
  AND event_date >= today() - 1
  AND query_duration_ms > 1000
  AND query NOT LIKE '%system.%'
ORDER BY query_duration_ms DESC
LIMIT 20
SETTINGS allow_introspection_functions = 1

Step 2 — Execute query with profiling

Generate a unique query ID and run the query with aggressive profiling settings (100us sampling = ~10,000 samples/sec).

Use clickhouse-client in non-interactive mode with an explicit --query_id to avoid any race with concurrent queries:

PROFILE_QID="cpu-profile-$(uuidgen)"
clickhouse-client --query_id "$PROFILE_QID" -q "
    SELECT ...
    SETTINGS query_profiler_cpu_time_period_ns = 100000,
             query_profiler_real_time_period_ns = 100000
"

Alternatively, if running interactively, parse the Query id: <uuid> line that clickhouse-client prints before each query.

After execution, verify the query completed and collect metadata:

SELECT query_id, query_duration_ms, formatReadableSize(memory_usage) AS peak_memory
FROM system.query_log
WHERE type = 'QueryFinish' AND query_id = '{query_id}'
SETTINGS allow_introspection_functions = 1

Wait 2 seconds for trace_log to flush, then proceed to Step 3.

Step 3 — Collect and analyze trace data

Run these analyses in parallel using Task tool (3 tasks):

Agent A — Top functions by CPU samples

SELECT
    count() AS samples,
    round(100.0 * count() / (SELECT count() FROM system.trace_log WHERE query_id = '{query_id}' AND trace_type = 'CPU'), 2) AS pct,
    demangle(addressToSymbol(trace[1])) AS function
FROM system.trace_log
WHERE query_id = '{query_id}'
  AND trace_type = 'CPU'
GROUP BY function
ORDER BY samples DESC
LIMIT 30
SETTINGS allow_introspection_functions = 1

Agent B — Top stack traces (full call paths)

SELECT
    count() AS samples,
    arrayStringConcat(
        arrayMap(x -> demangle(addressToSymbol(x)), trace),
        '\n    '
    ) AS stack
FROM system.trace_log
WHERE query_id = '{query_id}'
  AND trace_type = 'CPU'
GROUP BY trace
ORDER BY samples DESC
LIMIT 15
SETTINGS allow_introspection_functions = 1

Agent C — Export collapsed stacks for flamegraph

SELECT
    concat(
        arrayStringConcat(
            arrayReverse(arrayMap(x -> demangle(addressToSymbol(x)), trace)),
            ';'
        ),
        ' ',
        toString(count())
    )
FROM system.trace_log
WHERE query_id = '{query_id}'
  AND trace_type = 'CPU'
GROUP BY trace
ORDER BY count() DESC
SETTINGS allow_introspection_functions = 1
FORMAT TSVRaw

Save this output to tmp/cpu_profile_{query_id}.collapsed for optional flamegraph generation.

Also collect metadata:

SELECT
    count() AS total_samples,
    min(event_time_microseconds) AS first_sample,
    max(event_time_microseconds) AS last_sample,
    dateDiff('millisecond', min(event_time_microseconds), max(event_time_microseconds)) AS profile_duration_ms
FROM system.trace_log
WHERE query_id = '{query_id}' AND trace_type = 'CPU'
SETTINGS allow_introspection_functions = 1

Step 4 — Synthesize results

Using outputs from all three agents, produce a structured report:

1. Profile summary: query_id, total samples, profile duration, sampling rate
2. Top 15 CPU hotspot functions with sample count and percentage — as a table
3. Top 5 full stack traces with readable formatting — show the call chain from outermost to innermost
4. Subsystem breakdown: Group functions into categories:
   • Query Execution (HashJoin, Aggregator, MergeSorter, etc.)
   • Expression Evaluation (ExpressionActions, functions)
   • IO (ReadBuffer, WriteBuffer, S3, disk)
   • Network (Exchange, Connection, Protocol)
   • Compression (LZ4, ZSTD, codecs)
   • Memory Management (Arena, Allocator, PODArray)
   • Optimizer (Cascades, JoinOrder, Statistics)
   • Other
5. Actionable findings: What's unexpectedly hot, what could be optimized
6. Collapsed stack file location for flamegraph generation

Step 5 — Offer drill-down options

Ask the user:

• "Drill into a function": Filter traces containing a specific function name
• "Compare CPU vs Real time": Run the same analysis for trace_type = 'Real' to find wall-clock hotspots (IO waits, lock contention)
• "Generate flamegraph": If flamegraph.pl is available, render SVG:

  flamegraph.pl --title "CPU Profile: {query_id}" --countname samples --width 1800 \
    tmp/cpu_profile_{query_id}.collapsed > tmp/cpu_flamegraph_{query_id}.svg
  

  Or suggest using https://www.speedscope.app with the collapsed file.
• "Show source locations": Re-run with addressToLine for source file:line mapping:

  SELECT
      count() AS samples,
      demangle(addressToSymbol(trace[1])) AS function,
      addressToLine(trace[1]) AS source_location
  FROM system.trace_log
  WHERE query_id = '{query_id}' AND trace_type = 'CPU'
  GROUP BY function, source_location
  ORDER BY samples DESC
  LIMIT 30
  SETTINGS allow_introspection_functions = 1
  

• "Done": Exit

Repeat drill-down until user selects "Done".

Notes

• The query_profiler_cpu_time_period_ns setting controls sampling frequency. Default is 1,000,000,000 (1 sample/sec). Use 100,000 (100us) for detailed profiling of short queries, 1,000,000 (1ms) for longer queries.
• trace_type = 'CPU' counts CPU time; trace_type = 'Real' counts wall-clock time (includes IO waits).
• allow_introspection_functions = 1 is required for addressToSymbol, demangle, addressToLine.
• The clickhouse-common-static-dbg package must be installed for symbol resolution.
• On ClickHouse Cloud, use FROM clusterAllReplicas(default, system.trace_log) to collect traces from all nodes.
• Stack traces in system.trace_log are stored as arrays of addresses, with index 1 being the innermost (leaf) frame.

Examples

• /cpu-profile — Interactive: choose a query to profile
• /cpu-profile a1b2c3d4-e5f6-7890-abcd-ef1234567890 — Analyze existing traces for a query_id
• /cpu-profile SELECT count() FROM lineitem WHERE l_shipdate > '1995-01-01' — Execute and profile a query